Overview

Dataset statistics

Number of variables18
Number of observations1000
Missing cells253
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.8 KiB
Average record size in memory144.1 B

Variable types

Numeric11
Text1
Categorical6

Alerts

LastName has 22 (2.2%) missing valuesMissing
Location has 15 (1.5%) missing valuesMissing
Gender has 14 (1.4%) missing valuesMissing
Age has 24 (2.4%) missing valuesMissing
LoyaltyYears has 23 (2.3%) missing valuesMissing
AccountBalance has 27 (2.7%) missing valuesMissing
ProductCount has 11 (1.1%) missing valuesMissing
HasCreditCard has 18 (1.8%) missing valuesMissing
IncomeEstimate has 14 (1.4%) missing valuesMissing
HasComplaint has 20 (2.0%) missing valuesMissing
CardType has 13 (1.3%) missing valuesMissing
CreditCardPoints has 22 (2.2%) missing valuesMissing
RecordNumber is uniformly distributedUniform
CustomerId is uniformly distributedUniform
RecordNumber has unique valuesUnique
CustomerId has unique valuesUnique
AnnualSpending has unique valuesUnique
RiskScore has 16 (1.6%) zerosZeros
LoyaltyYears has 45 (4.5%) zerosZeros
ComplaintSatisfaction has 118 (11.8%) zerosZeros

Reproduction

Analysis started2024-06-30 10:49:01.281082
Analysis finished2024-06-30 10:49:07.754623
Duration6.47 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

RecordNumber
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-06-30T13:49:07.792617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.57706181
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.667
MonotonicityStrictly increasing
2024-06-30T13:49:07.849615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
672 1
 
0.1%
659 1
 
0.1%
660 1
 
0.1%
661 1
 
0.1%
662 1
 
0.1%
663 1
 
0.1%
664 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%

CustomerId
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1499.5
Minimum1000
Maximum1999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-06-30T13:49:07.951968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1049.95
Q11249.75
median1499.5
Q31749.25
95-th percentile1949.05
Maximum1999
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.19261049
Kurtosis-1.2
Mean1499.5
Median Absolute Deviation (MAD)250
Skewness0
Sum1499500
Variance83416.667
MonotonicityStrictly increasing
2024-06-30T13:49:08.007721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 1
 
0.1%
1671 1
 
0.1%
1658 1
 
0.1%
1659 1
 
0.1%
1660 1
 
0.1%
1661 1
 
0.1%
1662 1
 
0.1%
1663 1
 
0.1%
1664 1
 
0.1%
1665 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1000 1
0.1%
1001 1
0.1%
1002 1
0.1%
1003 1
0.1%
1004 1
0.1%
1005 1
0.1%
1006 1
0.1%
1007 1
0.1%
1008 1
0.1%
1009 1
0.1%
ValueCountFrequency (%)
1999 1
0.1%
1998 1
0.1%
1997 1
0.1%
1996 1
0.1%
1995 1
0.1%
1994 1
0.1%
1993 1
0.1%
1992 1
0.1%
1991 1
0.1%
1990 1
0.1%

LastName
Text

MISSING 

Distinct479
Distinct (%)49.0%
Missing22
Missing (%)2.2%
Memory size7.9 KiB
2024-06-30T13:49:08.185413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length8
Mean length6.1237219
Min length2

Characters and Unicode

Total characters5989
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique268 ?
Unique (%)27.4%

Sample

1st rowJennings
2nd rowGallagher
3rd rowPham
4th rowHenderson
5th rowLambert
ValueCountFrequency (%)
smith 23
 
2.4%
johnson 18
 
1.8%
anderson 12
 
1.2%
jones 10
 
1.0%
rodriguez 10
 
1.0%
gonzalez 10
 
1.0%
williams 10
 
1.0%
brown 10
 
1.0%
miller 10
 
1.0%
lee 9
 
0.9%
Other values (469) 856
87.5%
2024-06-30T13:49:08.424327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 584
 
9.8%
n 523
 
8.7%
o 508
 
8.5%
r 498
 
8.3%
a 461
 
7.7%
s 354
 
5.9%
l 335
 
5.6%
i 332
 
5.5%
t 211
 
3.5%
d 160
 
2.7%
Other values (39) 2023
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5989
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 584
 
9.8%
n 523
 
8.7%
o 508
 
8.5%
r 498
 
8.3%
a 461
 
7.7%
s 354
 
5.9%
l 335
 
5.6%
i 332
 
5.5%
t 211
 
3.5%
d 160
 
2.7%
Other values (39) 2023
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5989
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 584
 
9.8%
n 523
 
8.7%
o 508
 
8.5%
r 498
 
8.3%
a 461
 
7.7%
s 354
 
5.9%
l 335
 
5.6%
i 332
 
5.5%
t 211
 
3.5%
d 160
 
2.7%
Other values (39) 2023
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5989
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 584
 
9.8%
n 523
 
8.7%
o 508
 
8.5%
r 498
 
8.3%
a 461
 
7.7%
s 354
 
5.9%
l 335
 
5.6%
i 332
 
5.5%
t 211
 
3.5%
d 160
 
2.7%
Other values (39) 2023
33.8%

RiskScore
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)10.2%
Missing10
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean49.782828
Minimum0
Maximum100
Zeros16
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-06-30T13:49:08.504533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median50
Q375
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)52

Descriptive statistics

Standard deviation29.916708
Coefficient of variation (CV)0.60094432
Kurtosis-1.2073991
Mean49.782828
Median Absolute Deviation (MAD)26
Skewness0.026746746
Sum49285
Variance895.00941
MonotonicityNot monotonic
2024-06-30T13:49:08.560223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 19
 
1.9%
89 19
 
1.9%
32 18
 
1.8%
98 17
 
1.7%
91 16
 
1.6%
100 16
 
1.6%
0 16
 
1.6%
57 15
 
1.5%
18 14
 
1.4%
23 14
 
1.4%
Other values (91) 826
82.6%
ValueCountFrequency (%)
0 16
1.6%
1 13
1.3%
2 12
1.2%
3 11
1.1%
4 11
1.1%
5 8
0.8%
6 8
0.8%
7 12
1.2%
8 10
1.0%
9 5
 
0.5%
ValueCountFrequency (%)
100 16
1.6%
99 8
0.8%
98 17
1.7%
97 7
0.7%
96 9
0.9%
95 13
1.3%
94 10
1.0%
93 7
0.7%
92 11
1.1%
91 16
1.6%

Location
Categorical

MISSING 

Distinct20
Distinct (%)2.0%
Missing15
Missing (%)1.5%
Memory size7.9 KiB
Charlotte
 
60
San Diego
 
58
Phoenix
 
58
Indianapolis
 
58
Los Angeles
 
55
Other values (15)
696 

Length

Max length13
Median length11
Mean length8.9076142
Min length6

Characters and Unicode

Total characters8774
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndianapolis
2nd rowSan Antonio
3rd rowJacksonville
4th rowIndianapolis
5th rowFort Worth

Common Values

ValueCountFrequency (%)
Charlotte 60
 
6.0%
San Diego 58
 
5.8%
Phoenix 58
 
5.8%
Indianapolis 58
 
5.8%
Los Angeles 55
 
5.5%
Philadelphia 53
 
5.3%
Jacksonville 52
 
5.2%
Fort Worth 50
 
5.0%
Austin 50
 
5.0%
Chicago 49
 
4.9%
Other values (10) 442
44.2%

Length

2024-06-30T13:49:08.613067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san 169
 
12.9%
charlotte 60
 
4.6%
diego 58
 
4.4%
phoenix 58
 
4.4%
indianapolis 58
 
4.4%
los 55
 
4.2%
angeles 55
 
4.2%
philadelphia 53
 
4.1%
jacksonville 52
 
4.0%
fort 50
 
3.8%
Other values (14) 640
48.9%

Most occurring characters

ValueCountFrequency (%)
o 878
 
10.0%
n 801
 
9.1%
a 765
 
8.7%
e 676
 
7.7%
i 605
 
6.9%
l 569
 
6.5%
s 524
 
6.0%
t 503
 
5.7%
h 372
 
4.2%
323
 
3.7%
Other values (25) 2758
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 878
 
10.0%
n 801
 
9.1%
a 765
 
8.7%
e 676
 
7.7%
i 605
 
6.9%
l 569
 
6.5%
s 524
 
6.0%
t 503
 
5.7%
h 372
 
4.2%
323
 
3.7%
Other values (25) 2758
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 878
 
10.0%
n 801
 
9.1%
a 765
 
8.7%
e 676
 
7.7%
i 605
 
6.9%
l 569
 
6.5%
s 524
 
6.0%
t 503
 
5.7%
h 372
 
4.2%
323
 
3.7%
Other values (25) 2758
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 878
 
10.0%
n 801
 
9.1%
a 765
 
8.7%
e 676
 
7.7%
i 605
 
6.9%
l 569
 
6.5%
s 524
 
6.0%
t 503
 
5.7%
h 372
 
4.2%
323
 
3.7%
Other values (25) 2758
31.4%

Gender
Categorical

MISSING 

Distinct2
Distinct (%)0.2%
Missing14
Missing (%)1.4%
Memory size7.9 KiB
Female
513 
Male
473 

Length

Max length6
Median length6
Mean length5.040568
Min length4

Characters and Unicode

Total characters4970
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 513
51.3%
Male 473
47.3%
(Missing) 14
 
1.4%

Length

2024-06-30T13:49:08.663640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:08.708370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
female 513
52.0%
male 473
48.0%

Most occurring characters

ValueCountFrequency (%)
e 1499
30.2%
a 986
19.8%
l 986
19.8%
F 513
 
10.3%
m 513
 
10.3%
M 473
 
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1499
30.2%
a 986
19.8%
l 986
19.8%
F 513
 
10.3%
m 513
 
10.3%
M 473
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1499
30.2%
a 986
19.8%
l 986
19.8%
F 513
 
10.3%
m 513
 
10.3%
M 473
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1499
30.2%
a 986
19.8%
l 986
19.8%
F 513
 
10.3%
m 513
 
10.3%
M 473
 
9.5%

Age
Real number (ℝ)

MISSING 

Distinct53
Distinct (%)5.4%
Missing24
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean44.705943
Minimum18
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-06-30T13:49:08.752880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q132
median45
Q358
95-th percentile68
Maximum70
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.308138
Coefficient of variation (CV)0.34241841
Kurtosis-1.2027176
Mean44.705943
Median Absolute Deviation (MAD)13
Skewness-0.06299281
Sum43633
Variance234.33908
MonotonicityNot monotonic
2024-06-30T13:49:08.808396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 28
 
2.8%
66 28
 
2.8%
38 27
 
2.7%
62 26
 
2.6%
52 26
 
2.6%
64 24
 
2.4%
68 24
 
2.4%
21 23
 
2.3%
36 23
 
2.3%
58 22
 
2.2%
Other values (43) 725
72.5%
(Missing) 24
 
2.4%
ValueCountFrequency (%)
18 17
1.7%
19 14
1.4%
20 19
1.9%
21 23
2.3%
22 17
1.7%
23 19
1.9%
24 12
1.2%
25 20
2.0%
26 19
1.9%
27 11
1.1%
ValueCountFrequency (%)
70 18
1.8%
69 15
1.5%
68 24
2.4%
67 13
1.3%
66 28
2.8%
65 18
1.8%
64 24
2.4%
63 22
2.2%
62 26
2.6%
61 17
1.7%

LoyaltyYears
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)2.1%
Missing23
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean10.117707
Minimum0
Maximum20
Zeros45
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-06-30T13:49:08.855523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0658497
Coefficient of variation (CV)0.59952809
Kurtosis-1.2179167
Mean10.117707
Median Absolute Deviation (MAD)5
Skewness-0.039623757
Sum9885
Variance36.794532
MonotonicityNot monotonic
2024-06-30T13:49:08.906313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
16 59
 
5.9%
14 56
 
5.6%
12 55
 
5.5%
8 53
 
5.3%
3 52
 
5.2%
18 50
 
5.0%
9 48
 
4.8%
10 48
 
4.8%
1 48
 
4.8%
20 47
 
4.7%
Other values (11) 461
46.1%
ValueCountFrequency (%)
0 45
4.5%
1 48
4.8%
2 43
4.3%
3 52
5.2%
4 42
4.2%
5 46
4.6%
6 38
3.8%
7 44
4.4%
8 53
5.3%
9 48
4.8%
ValueCountFrequency (%)
20 47
4.7%
19 43
4.3%
18 50
5.0%
17 45
4.5%
16 59
5.9%
15 46
4.6%
14 56
5.6%
13 33
3.3%
12 55
5.5%
11 36
3.6%

AccountBalance
Real number (ℝ)

MISSING 

Distinct966
Distinct (%)99.3%
Missing27
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean50970.421
Minimum1203
Maximum99618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-06-30T13:49:08.966714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1203
5-th percentile6346
Q126613
median50422
Q376209
95-th percentile95015.6
Maximum99618
Range98415
Interquartile range (IQR)49596

Descriptive statistics

Standard deviation28218.405
Coefficient of variation (CV)0.55362315
Kurtosis-1.1810301
Mean50970.421
Median Absolute Deviation (MAD)24354
Skewness0.0088795456
Sum49594220
Variance7.9627838 × 108
MonotonicityNot monotonic
2024-06-30T13:49:09.022994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44856 2
 
0.2%
2881 2
 
0.2%
28526 2
 
0.2%
45386 2
 
0.2%
48574 2
 
0.2%
17999 2
 
0.2%
81072 2
 
0.2%
35812 1
 
0.1%
94326 1
 
0.1%
52493 1
 
0.1%
Other values (956) 956
95.6%
(Missing) 27
 
2.7%
ValueCountFrequency (%)
1203 1
0.1%
1307 1
0.1%
1336 1
0.1%
1570 1
0.1%
1917 1
0.1%
2051 1
0.1%
2587 1
0.1%
2609 1
0.1%
2630 1
0.1%
2657 1
0.1%
ValueCountFrequency (%)
99618 1
0.1%
99614 1
0.1%
99530 1
0.1%
99371 1
0.1%
99300 1
0.1%
99262 1
0.1%
99239 1
0.1%
99132 1
0.1%
99046 1
0.1%
99000 1
0.1%

ProductCount
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)1.0%
Missing11
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean5.512639
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-06-30T13:49:09.068328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8074316
Coefficient of variation (CV)0.5092718
Kurtosis-1.183063
Mean5.512639
Median Absolute Deviation (MAD)2
Skewness0.0031103057
Sum5452
Variance7.8816721
MonotonicityNot monotonic
2024-06-30T13:49:09.107524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 116
11.6%
7 112
11.2%
4 109
10.9%
2 102
10.2%
3 101
10.1%
9 100
10.0%
10 92
9.2%
8 88
8.8%
1 85
8.5%
5 84
8.4%
(Missing) 11
 
1.1%
ValueCountFrequency (%)
1 85
8.5%
2 102
10.2%
3 101
10.1%
4 109
10.9%
5 84
8.4%
6 116
11.6%
7 112
11.2%
8 88
8.8%
9 100
10.0%
10 92
9.2%
ValueCountFrequency (%)
10 92
9.2%
9 100
10.0%
8 88
8.8%
7 112
11.2%
6 116
11.6%
5 84
8.4%
4 109
10.9%
3 101
10.1%
2 102
10.2%
1 85
8.5%

HasCreditCard
Categorical

MISSING 

Distinct2
Distinct (%)0.2%
Missing18
Missing (%)1.8%
Memory size7.9 KiB
1.0
516 
0.0
466 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2946
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 516
51.6%
0.0 466
46.6%
(Missing) 18
 
1.8%

Length

2024-06-30T13:49:09.150494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:09.184590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 516
52.5%
0.0 466
47.5%

Most occurring characters

ValueCountFrequency (%)
0 1448
49.2%
. 982
33.3%
1 516
 
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1448
49.2%
. 982
33.3%
1 516
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1448
49.2%
. 982
33.3%
1 516
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1448
49.2%
. 982
33.3%
1 516
 
17.5%

ActiveStatus
Categorical

Distinct2
Distinct (%)0.2%
Missing10
Missing (%)1.0%
Memory size7.9 KiB
0.0
507 
1.0
483 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2970
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 507
50.7%
1.0 483
48.3%
(Missing) 10
 
1.0%

Length

2024-06-30T13:49:09.224765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:09.260709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 507
51.2%
1.0 483
48.8%

Most occurring characters

ValueCountFrequency (%)
0 1497
50.4%
. 990
33.3%
1 483
 
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1497
50.4%
. 990
33.3%
1 483
 
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1497
50.4%
. 990
33.3%
1 483
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1497
50.4%
. 990
33.3%
1 483
 
16.3%

IncomeEstimate
Real number (ℝ)

MISSING 

Distinct986
Distinct (%)100.0%
Missing14
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean111423.32
Minimum20028
Maximum199965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-06-30T13:49:09.306150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20028
5-th percentile29371.75
Q167223.25
median113468
Q3155986.5
95-th percentile189287.5
Maximum199965
Range179937
Interquartile range (IQR)88763.25

Descriptive statistics

Standard deviation51192.649
Coefficient of variation (CV)0.45944286
Kurtosis-1.1700577
Mean111423.32
Median Absolute Deviation (MAD)44377.5
Skewness-0.071427738
Sum1.098634 × 108
Variance2.6206874 × 109
MonotonicityNot monotonic
2024-06-30T13:49:09.362310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122511 1
 
0.1%
139496 1
 
0.1%
74703 1
 
0.1%
114977 1
 
0.1%
67819 1
 
0.1%
179541 1
 
0.1%
154917 1
 
0.1%
130186 1
 
0.1%
43290 1
 
0.1%
132551 1
 
0.1%
Other values (976) 976
97.6%
(Missing) 14
 
1.4%
ValueCountFrequency (%)
20028 1
0.1%
20294 1
0.1%
20368 1
0.1%
20453 1
0.1%
20595 1
0.1%
20727 1
0.1%
20767 1
0.1%
20834 1
0.1%
21214 1
0.1%
21468 1
0.1%
ValueCountFrequency (%)
199965 1
0.1%
199593 1
0.1%
199412 1
0.1%
199006 1
0.1%
198780 1
0.1%
198428 1
0.1%
198394 1
0.1%
198379 1
0.1%
198330 1
0.1%
198157 1
0.1%

AnnualSpending
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50229.207
Minimum-19585.153
Maximum113025.42
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)0.7%
Memory size7.9 KiB
2024-06-30T13:49:09.468209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-19585.153
5-th percentile13510.229
Q134421.042
median50574.839
Q366943.877
95-th percentile86280.64
Maximum113025.42
Range132610.57
Interquartile range (IQR)32522.835

Descriptive statistics

Standard deviation22476.611
Coefficient of variation (CV)0.44748091
Kurtosis-0.46303474
Mean50229.207
Median Absolute Deviation (MAD)16330.748
Skewness0.01311881
Sum50229207
Variance5.0519804 × 108
MonotonicityNot monotonic
2024-06-30T13:49:09.525403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57486.34231 1
 
0.1%
51522.02452 1
 
0.1%
34962.0039 1
 
0.1%
35389.03897 1
 
0.1%
62369.12627 1
 
0.1%
44716.09718 1
 
0.1%
81183.16517 1
 
0.1%
40027.63228 1
 
0.1%
71143.32184 1
 
0.1%
18389.8171 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
-19585.15291 1
0.1%
-4941.208309 1
0.1%
-3461.743376 1
0.1%
-2994.060408 1
0.1%
-2608.622151 1
0.1%
-2087.86471 1
0.1%
-674.4777504 1
0.1%
592.1282066 1
0.1%
1217.742098 1
0.1%
1342.552228 1
0.1%
ValueCountFrequency (%)
113025.4215 1
0.1%
109221.0164 1
0.1%
109155.4414 1
0.1%
107626.5131 1
0.1%
107545.7933 1
0.1%
106196.4936 1
0.1%
105206.131 1
0.1%
102633.4915 1
0.1%
100493.8729 1
0.1%
100310.3679 1
0.1%

HasComplaint
Categorical

MISSING 

Distinct2
Distinct (%)0.2%
Missing20
Missing (%)2.0%
Memory size7.9 KiB
0.0
493 
1.0
487 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2940
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 493
49.3%
1.0 487
48.7%
(Missing) 20
 
2.0%

Length

2024-06-30T13:49:09.574781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:09.610026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 493
50.3%
1.0 487
49.7%

Most occurring characters

ValueCountFrequency (%)
0 1473
50.1%
. 980
33.3%
1 487
 
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1473
50.1%
. 980
33.3%
1 487
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1473
50.1%
. 980
33.3%
1 487
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1473
50.1%
. 980
33.3%
1 487
 
16.6%

ComplaintSatisfaction
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)1.1%
Missing10
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean4.9505051
Minimum0
Maximum10
Zeros118
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-06-30T13:49:09.645208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2655593
Coefficient of variation (CV)0.65964164
Kurtosis-1.2831705
Mean4.9505051
Median Absolute Deviation (MAD)3
Skewness-0.019227708
Sum4901
Variance10.663877
MonotonicityNot monotonic
2024-06-30T13:49:09.688041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 118
11.8%
8 102
10.2%
9 93
9.3%
10 91
9.1%
2 89
8.9%
6 87
8.7%
3 86
8.6%
4 82
8.2%
7 81
8.1%
5 81
8.1%
ValueCountFrequency (%)
0 118
11.8%
1 80
8.0%
2 89
8.9%
3 86
8.6%
4 82
8.2%
5 81
8.1%
6 87
8.7%
7 81
8.1%
8 102
10.2%
9 93
9.3%
ValueCountFrequency (%)
10 91
9.1%
9 93
9.3%
8 102
10.2%
7 81
8.1%
6 87
8.7%
5 81
8.1%
4 82
8.2%
3 86
8.6%
2 89
8.9%
1 80
8.0%

CardType
Categorical

MISSING 

Distinct4
Distinct (%)0.4%
Missing13
Missing (%)1.3%
Memory size7.9 KiB
American Express
262 
Visa
250 
MasterCard
242 
Discover
233 

Length

Max length16
Median length10
Mean length9.6008105
Min length4

Characters and Unicode

Total characters9476
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmerican Express
2nd rowAmerican Express
3rd rowDiscover
4th rowDiscover
5th rowMasterCard

Common Values

ValueCountFrequency (%)
American Express 262
26.2%
Visa 250
25.0%
MasterCard 242
24.2%
Discover 233
23.3%
(Missing) 13
 
1.3%

Length

2024-06-30T13:49:09.734625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T13:49:09.774551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
american 262
21.0%
express 262
21.0%
visa 250
20.0%
mastercard 242
19.4%
discover 233
18.7%

Most occurring characters

ValueCountFrequency (%)
s 1249
13.2%
r 1241
13.1%
e 999
 
10.5%
a 996
 
10.5%
i 745
 
7.9%
c 495
 
5.2%
A 262
 
2.8%
m 262
 
2.8%
p 262
 
2.8%
x 262
 
2.8%
Other values (11) 2703
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 1249
13.2%
r 1241
13.1%
e 999
 
10.5%
a 996
 
10.5%
i 745
 
7.9%
c 495
 
5.2%
A 262
 
2.8%
m 262
 
2.8%
p 262
 
2.8%
x 262
 
2.8%
Other values (11) 2703
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 1249
13.2%
r 1241
13.1%
e 999
 
10.5%
a 996
 
10.5%
i 745
 
7.9%
c 495
 
5.2%
A 262
 
2.8%
m 262
 
2.8%
p 262
 
2.8%
x 262
 
2.8%
Other values (11) 2703
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 1249
13.2%
r 1241
13.1%
e 999
 
10.5%
a 996
 
10.5%
i 745
 
7.9%
c 495
 
5.2%
A 262
 
2.8%
m 262
 
2.8%
p 262
 
2.8%
x 262
 
2.8%
Other values (11) 2703
28.5%

CreditCardPoints
Real number (ℝ)

MISSING 

Distinct893
Distinct (%)91.3%
Missing22
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2447.364
Minimum2
Maximum4994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-06-30T13:49:09.825284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile251.1
Q11167.75
median2395.5
Q33679.75
95-th percentile4750.05
Maximum4994
Range4992
Interquartile range (IQR)2512

Descriptive statistics

Standard deviation1451.0669
Coefficient of variation (CV)0.59291011
Kurtosis-1.1826114
Mean2447.364
Median Absolute Deviation (MAD)1261
Skewness0.059271488
Sum2393522
Variance2105595
MonotonicityNot monotonic
2024-06-30T13:49:09.882062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
784 3
 
0.3%
1914 3
 
0.3%
761 3
 
0.3%
3598 3
 
0.3%
661 2
 
0.2%
4062 2
 
0.2%
1515 2
 
0.2%
4570 2
 
0.2%
4696 2
 
0.2%
2428 2
 
0.2%
Other values (883) 954
95.4%
(Missing) 22
 
2.2%
ValueCountFrequency (%)
2 1
0.1%
11 1
0.1%
23 1
0.1%
25 1
0.1%
26 1
0.1%
36 1
0.1%
39 1
0.1%
43 1
0.1%
51 2
0.2%
56 1
0.1%
ValueCountFrequency (%)
4994 1
0.1%
4984 1
0.1%
4971 1
0.1%
4970 1
0.1%
4969 1
0.1%
4960 1
0.1%
4952 1
0.1%
4944 1
0.1%
4923 1
0.1%
4916 2
0.2%

Interactions

2024-06-30T13:49:06.989606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:01.469772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.311012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.912719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.391529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.976477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.466648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.931988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.427673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.923984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.404831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:07.031445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:01.570719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.392587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.952595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.431410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.019522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.508635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.975471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.470647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.966935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.446259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:07.072299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:01.727903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.469717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.993475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.472107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.063468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.550113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.015522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.515078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.009417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.487518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:07.112710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:01.777731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.575387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.033225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.511610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.106500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.591954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.054606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.557873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.051986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.528274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:07.154095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:01.832048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.615447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.073723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.553595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.149995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.632574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.094731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.603410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.093281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.578499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:07.199561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:01.890363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.660807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.143370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.695006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.196785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.677689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.138916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.658875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.139582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.637495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:07.241713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:01.958969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.702500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.184732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.739907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.242748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.718741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.226181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.702908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.182417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.718721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:07.281186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.015036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.741589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.223565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.809135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.284716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.757476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.262532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.744718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.223082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.770693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:07.326526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.103570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.787238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.268517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.853846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.332757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.803345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.306571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.791290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.272430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.865957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:07.369564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.167874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.830257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.310712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.896236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.378341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.845458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.348234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.836414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.319966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.907380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:07.411604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.237270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:02.871985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.350539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:03.936142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.422716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:04.887266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.387176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:05.880826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.362712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-30T13:49:06.948858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-30T13:49:07.475061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-30T13:49:07.582625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-30T13:49:07.690513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

RecordNumberCustomerIdLastNameRiskScoreLocationGenderAgeLoyaltyYearsAccountBalanceProductCountHasCreditCardActiveStatusIncomeEstimateAnnualSpendingHasComplaintComplaintSatisfactionCardTypeCreditCardPoints
011000Jennings51.0IndianapolisFemale36.010.033172.01.01.00.0122511.057486.3423140.010.0American Express4181.0
121001Gallagher92.0San AntonioFemale66.02.060197.08.00.01.0113144.029617.1693700.010.0American Express4858.0
231002Pham14.0JacksonvilleFemale63.07.059847.04.00.00.084824.053352.9441191.0NaNNaN389.0
341003Henderson71.0IndianapolisFemale36.02.012577.05.00.01.043525.07072.4329531.04.0Discover718.0
451004Lambert60.0Fort WorthMale39.013.078299.08.01.00.077804.054096.2274680.06.0Discover4244.0
561005Hill20.0WashingtonFemale41.012.021176.05.01.00.0166115.071312.7878070.02.0MasterCard4678.0
671006Simpson82.0HoustonFemale62.012.045386.010.00.01.061846.020237.6291441.08.0Discover120.0
781007Rodriguez86.0San JoseMale29.01.064972.04.00.00.097261.055154.1495240.07.0American Express2652.0
891008Alexander74.0ChicagoMale47.012.090791.06.01.01.0188455.086032.3675910.02.0Discover2303.0
9101009Roman74.0DallasMale55.018.042003.05.01.00.049483.0592.1282071.04.0Visa4861.0
RecordNumberCustomerIdLastNameRiskScoreLocationGenderAgeLoyaltyYearsAccountBalanceProductCountHasCreditCardActiveStatusIncomeEstimateAnnualSpendingHasComplaintComplaintSatisfactionCardTypeCreditCardPoints
9909911990Jones17.0SeattleFemale57.016.033006.01.00.01.065591.025514.2248850.02.0American Express3909.0
9919921991Soto37.0San AntonioFemale40.08.032422.08.00.01.081750.041239.1745541.00.0American Express4801.0
9929931992Gutierrez98.0CharlotteFemale26.01.013205.07.01.01.0110648.026972.7311000.07.0Visa1164.0
9939941993Boyd14.0AustinFemale51.014.087590.03.0NaN0.0116134.061069.4271021.05.0MasterCard619.0
9949951994Harper63.0SeattleFemale63.015.024527.06.01.01.0186526.068421.9656870.01.0American Express3066.0
9959961995Adams88.0JacksonvilleFemale31.03.019443.09.01.01.0118537.040494.2813390.07.0Visa4872.0
9969971996Smith100.0PhiladelphiaFemale21.07.017524.07.00.01.082672.019895.8179650.05.0MasterCard446.0
9979981997Diaz27.0WashingtonMale47.0NaN29202.03.01.00.047228.019413.2405380.06.0American Express2337.0
9989991998Peterson73.0Fort WorthMale48.04.066552.06.01.01.034429.040553.9553030.010.0American Express208.0
99910001999Horton38.0WashingtonFemale55.015.047142.04.00.01.0104294.051282.5131650.00.0American Express2460.0